---
title: Regression Models Fine-Tuning
sidebarTitle: Regression Models Fine-Tuning
---


In this example, we use our sample PostgreSQL database. You can connect to it like this:

```sql
CREATE DATABASE example_db
WITH ENGINE = "postgres",
PARAMETERS = {
    "user": "demo_user",
    "password": "demo_password",
    "host": "3.220.66.106",
    "port": "5432",
    "database": "demo"
    };
```

First, we create and train the model using a subset of the `home_rentals` data, considering properties that have been on the market less than 10 days.

```sql
CREATE MODEL mindsdb.adjust_home_rentals_model
FROM example_db
    (SELECT * 
    FROM demo_data.home_rentals 
    WHERE days_on_market < 10)
PREDICT rental_price;
```

On execution, we get:

```sql
Query successfully completed
```

We can check its status using this command:

```sql
SELECT *
FROM mindsdb.models
WHERE name = 'adjust_home_rentals_model';
```

Once the status is complete, we can query for predictions.

```sql
SELECT rental_price, rental_price_explain 
FROM mindsdb.adjust_home_rentals_model
WHERE sqft = 1000
AND location = 'great'
AND neighborhood = 'berkeley_hills'
AND number_of_rooms = 2
AND number_of_bathrooms = 1
AND days_on_market = 40;
```

On execution, we get:

```sql
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| rental_price  | rental_price_explain                                                                                                                          |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| 2621          | {"predicted_value": 2621, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 2523, "confidence_upper_bound": 2719} |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
```
 
Let’s adjust this model with more training data. Now we consider properties that have been on the market for 10 or more days.

```sql
FINETUNE mindsdb.adjust_home_rentals_model
FROM example_db
    (SELECT * 
    FROM demo_data.home_rentals 
    WHERE days_on_market >= 10);
```

<Warning>
While the model is being generated and trained, it is not active. The model becomes active only after it completes generating and training.
</Warning>

To check the status and versions of the model, run this command:

```sql
SELECT name, engine, project, active, version, status
FROM mindsdb.models_versions
WHERE name = 'adjust_home_rentals_model';
```

On execution, we get:

```sql
+---------------------------+-----------+---------+--------+---------+----------+
| name                      | engine    | project | active | version | status   |
+---------------------------+-----------+---------+--------+---------+----------+
| adjust_home_rentals_model | lightwood | mindsdb | false  | 1       | complete |
| adjust_home_rentals_model | lightwood | mindsdb | true   | 2       | complete |
+---------------------------+-----------+---------+--------+---------+----------+
```

Please note that the longer the property is on the market, the lower its rental price. Hence, we can expect the `rental_price` prediction to be lower.

```sql
SELECT rental_price, rental_price_explain 
FROM mindsdb.adjust_home_rentals_model
WHERE sqft = 1000
AND location = 'great'
AND neighborhood = 'berkeley_hills'
AND number_of_rooms = 2
AND number_of_bathrooms = 1
AND days_on_market = 40;
```

On execution, we get:

```sql
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| rental_price  | rental_price_explain                                                                                                                          |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| 2055          | {"predicted_value": 2055, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 1957, "confidence_upper_bound": 2153} |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
```